Update app.py

app.py

@@ -14,190 +14,10 @@ import tempfile
 import io
 import logging
 
-# -----------------------------
-# Google Gemini API 관련
-# -----------------------------
-import google.generativeai as genai
-import google.generativeai.types as genai_types
-
 logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')
 
 ###############################################################################
-# 1. 텍스트(한글 → 영어) 변환 보조 함수
-###############################################################################
-
-def maybe_translate_to_english(text: str) -> str:
-    """
-    텍스트에 한국어가 있으면 간단한 치환 규칙에 따라 영어로 변환.
-    """
-    translations = {
-        "안녕하세요": "Hello",
-        "환영합니다": "Welcome",
-        "안녕": "Hello",
-        "배너": "Banner",
-        # 필요에 따라 추가
-    }
-    for kr, en in translations.items():
-        if kr in text:
-            text = text.replace(kr, en)
-    return text
-
-###############################################################################
-# 2. Gemini API 호출을 위한 준비
-###############################################################################
-
-def save_binary_file(file_name, data):
-    """ 이진 파일을 저장하는 헬퍼 함수 """
-    with open(file_name, "wb") as f:
-        f.write(data)
-
-def generate_by_google_genai(text, file_name, model="gemini-2.0-flash-exp"):
-    """
-    Google Gemini API를 호출해 텍스트 기반 이미지 편집/생성을 수행.
-    file_name: 원본 이미지를 임시 업로드하여 API로 전달
-    text: 적용할 텍스트 지시사항
-    """
-    api_key = os.getenv("GAPI_TOKEN")
-    if not api_key:
-        raise ValueError("GAPI_TOKEN is missing. Please set an API key.")
-
-    # Gemini API 인증 설정
-    genai.configure(api_key=api_key)
-
-    # 이미지 파일 업로드
-    with open(file_name, "rb") as f:
-        image_data = f.read()
-    
-    # 최신 Gemini API 구조에 맞춰 요청 구성
-    model = genai.GenerativeModel(model)
-    
-    # API에 전달할 content 구성
-    image_parts = [
-        {
-            "mime_type": "image/png",
-            "data": image_data
-        },
-        text
-    ]
-    
-    # 생성(편집) 설정
-    generation_config = {
-        "temperature": 1,
-        "top_p": 0.95,
-        "top_k": 40,
-        "max_output_tokens": 8192,
-    }
-
-    text_response = ""   # API가 반환한 텍스트 누적
-    image_path = None    # API가 반환한 이미지 파일의 로컬 경로
-
-    # 임시 파일에 편집된 이미지 저장
-    with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp:
-        temp_path = tmp.name
-
-        try:
-            # 스트리밍 형태로 응답을 받음
-            response = model.generate_content(
-                contents=image_parts,
-                generation_config=generation_config,
-                stream=True
-            )
-
-            # 스트리밍된 chunk들에서 이미지와 텍스트를 추출
-            for chunk in response:
-                # 이미지 데이터가 있는지 확인
-                if hasattr(chunk, 'parts'):
-                    for part in chunk.parts:
-                        if hasattr(part, 'inline_data') and part.inline_data:
-                            save_binary_file(temp_path, part.inline_data.data)
-                            image_path = temp_path
-                        elif hasattr(part, 'text'):
-                            text_response += part.text
-        
-        except Exception as e:
-            logging.error(f"Gemini API error: {e}")
-            return None, str(e)
-
-    return image_path, text_response
-
-###############################################################################
-# 3. 이미지에 텍스트를 삽입/수정하는 함수 (Gemini API 2회 호출)
-###############################################################################
-
-def change_text_in_image_two_times(original_image, instruction):
-    """
-    Gemini API를 두 번 호출하여 두 개의 버전을 생성한다.
-    """
-    import numpy as np
-
-    # 만약 이미지가 numpy.ndarray 타입이면 PIL로 변환
-    if isinstance(original_image, np.ndarray):
-        original_image = Image.fromarray(original_image)
-    
-    results = []
-    for version_tag in ["(A)", "(B)"]:
-        mod_instruction = f"{instruction} {version_tag}"
-        try:
-            with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp:
-                original_path = tmp.name
-                if isinstance(original_image, Image.Image):
-                    original_image.save(original_path, format="PNG")
-                    logging.debug(f"[DEBUG] Saved image to temporary file: {original_path}")
-                else:
-                    raise gr.Error(f"예상된 PIL Image가 아닌 {type(original_image)} 타입이 제공되었습니다.")
-            # Gemini API 호출
-            image_path, text_response = generate_by_google_genai(
-                text=mod_instruction,
-                file_name=original_path
-            )
-            if image_path:
-                # 반환된 이미지 로드
-                try:
-                    with open(image_path, "rb") as f:
-                        image_data = f.read()
-                    new_img = Image.open(io.BytesIO(image_data))
-                    results.append(new_img)
-                except Exception as img_err:
-                    logging.error(f"[ERROR] Failed to process Gemini image: {img_err}")
-                    results.append(original_image)
-            else:
-                logging.warning(f"[WARNING] 이미지가 반환되지 않았습니다. 텍스트 응답: {text_response}")
-                results.append(original_image)
-        except Exception as e:
-            logging.exception(f"Text modification error: {e}")
-            results.append(original_image)
-    return results
-
-###############################################################################
-# 4. 텍스트 렌더링(문자 삽입)용 함수
-###############################################################################
-
-def gemini_text_rendering(image, rendering_text):
-    """
-    주어진 image에 대해 Gemini API로 text_rendering을 적용
-    """
-    rendering_text_en = maybe_translate_to_english(rendering_text)
-    instruction = (
-        f"Render the following text on the image in a clear, visually appealing manner: "
-        f"{rendering_text_en}."
-    )
-    # 이미지에 텍스트 삽입(A/B 버전 2회 생성) → 여기서는 2회 중 첫 번째만 반환
-    rendered_images = change_text_in_image_two_times(image, instruction)
-    if rendered_images and len(rendered_images) > 0:
-        return rendered_images[0]
-    return image
-
-def apply_text_rendering(image, rendering_text):
-    """
-    rendering_text가 존재하면 Gemini API로 텍스트 삽입을 적용.
-    없으면 원본 이미지를 그대로 반환.
-    """
-    if rendering_text and rendering_text.strip():
-        return gemini_text_rendering(image, rendering_text)
-    return image
-
-###############################################################################
-# 5. Diffusion Pipeline 로드 및 기본 세팅
+# 1. Diffusion Pipeline 로드 및 기본 세팅
 ###############################################################################
 
 SAVE_DIR = "saved_images"
@@ -307,33 +127,91 @@ def inference(
         error_img = Image.new('RGB', (width, height), color='red')
         return error_img, seed, load_generated_images()
 
-# 프롬프트 내 텍스트를 추출하여 텍스트 렌더링 입력란에 자동으로 채우는 함수
-def extract_text_for_rendering(prompt):
+###############################################################################
+# 2. 이미지 업로드 처리 함수 추가
+###############################################################################
+
+def process_uploaded_image(
+    image,
+    prompt: str,
+    seed: int,
+    randomize_seed: bool,
+    guidance_scale: float,
+    num_inference_steps: int,
+    lora_scale: float,
+    progress: gr.Progress = gr.Progress(track_tqdm=True),
+):
     """
-    프롬프트에서 따옴표('' 또는 "")로 둘러싸인 텍스트를 찾아
-    텍스트 렌더링 입력란에 자동으로 채우기 위한 함수
+    업로드된 이미지를 지브리 스타일로 변환하는 함수
     """
-    # 따옴표로 둘러싸인 텍스트 찾기
-    single_quotes = re.findall(r"'([^']*)'", prompt)
-    double_quotes = re.findall(r'"([^"]*)"', prompt)
+    if image is None:
+        return None, seed, load_generated_images()
     
-    # 추출된 텍스트가 있으면 첫 번째 것을 반환
-    extracted_texts = single_quotes + double_quotes
-    if extracted_texts:
-        return extracted_texts[0]
-    return ""
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    generator = torch.Generator(device=device).manual_seed(seed)
+    
+    try:
+        # 이미지 전처리
+        if isinstance(image, np.ndarray):
+            image_pil = Image.fromarray(image)
+        else:
+            image_pil = image
+        
+        # 이미지 크기 조정 (원본 비율 유지)
+        width, height = image_pil.size
+        max_size = 768
+        if width > height:
+            if width > max_size:
+                ratio = max_size / width
+                new_width = max_size
+                new_height = int(height * ratio)
+        else:
+            if height > max_size:
+                ratio = max_size / height
+                new_height = max_size
+                new_width = int(width * ratio)
+        
+        if width > max_size or height > max_size:
+            image_pil = image_pil.resize((new_width, new_height), Image.LANCZOS)
+        
+        # 프롬프트가 비어있는 경우 기본 프롬프트 사용
+        if not prompt or prompt.strip() == "":
+            prompt = "Ghibli style artwork, high quality, detailed"
+        elif "ghibli" not in prompt.lower() and "studio ghibli" not in prompt.lower():
+            prompt = f"Ghibli style {prompt}, high quality, detailed"
+        
+        # 이미지 생성 (img2img)
+        # 참고: 실제 FLUX 모델이 img2img를 지원하는지에 따라 이 부분은 조정 필요
+        image = pipeline(
+            prompt=prompt,
+            image=image_pil,  # 이미지 입력
+            guidance_scale=guidance_scale,
+            num_inference_steps=num_inference_steps,
+            generator=generator,
+            joint_attention_kwargs={"scale": lora_scale},
+            strength=0.75,  # 적용 강도 (0: 원본 유지, 1: 완전히 새로 생성)
+        ).images[0]
+
+        filepath = save_generated_image(image, f"Uploaded image with prompt: {prompt}")
+        return image, seed, load_generated_images()
+
+    except Exception as e:
+        logging.error(f"Error during image processing: {e}")
+        return image, seed, load_generated_images()
+
 
 ###############################################################################
-# 6. Gradio UI
+# 3. Gradio UI
 ###############################################################################
 
 examples = [
     "Ghibli style futuristic stormtrooper with glossy white armor and a sleek helmet, standing heroically on a lush alien planet, vibrant flowers blooming around, soft sunlight illuminating the scene, a gentle breeze rustling the leaves. The armor reflects the pink and purple hues of the alien sunset, creating an ethereal glow around the figure. [trigger]",
-    "Ghibli style young mechanic girl in a floating workshop, surrounded by hovering tools and glowing mechanical parts, her blue overalls covered in oil stains, tinkering with a semi-transparent robot companion. Magical sparks fly as she works, while floating islands with waterfalls drift past her open workshop window. [trigger]",
+    "Ghibli style wonder woman, she saying \"hello\" in 'speech bubble', her blue costume shining under the sun, surrounded by floating islands with waterfalls, magical sparkles in the air. [trigger]",
     "Ghibli style ancient forest guardian robot, covered in moss and flowering vines, sitting peacefully in a crystal-clear lake. Its gentle eyes glow with soft blue light, while bioluminescent dragonflies dance around its weathered metal frame. Ancient tech symbols on its surface pulse with a gentle rhythm. [trigger]",
-    "Ghibli style sky whale transport ship, its metallic skin adorned with traditional Japanese patterns, gliding through cotton candy clouds at sunrise. Small floating gardens hang from its sides, where workers in futuristic kimonos tend to glowing plants. Rainbow auroras shimmer in the background. [trigger]",
+    "Ghibli style superhero girl saying \"I will protect you\" in a 'speech bubble', her cape flowing in the wind as she stands on a rooftop at sunset, with city lights beginning to twinkle below. [trigger]",
     "Ghibli style cyber-shrine maiden with flowing holographic robes, performing a ritual dance among floating lanterns and digital cherry blossoms. Her traditional headdress emits soft light patterns, while spirit-like AI constructs swirl around her in elegant patterns. The scene is set in a modern shrine with both ancient wood and sleek chrome elements. [trigger]",
-    "Ghibli style robot farmer tending to floating rice paddies in the sky, wearing a traditional straw hat with advanced sensors. Its gentle movements create ripples in the water as it plants glowing rice seedlings. Flying fish leap between the terraced fields, leaving trails of sparkles in their wake, while future Tokyo's spires gleam in the distance. [trigger]"
+    "Ghibli style robot farmer saying \"Harvest time!\" in 'speech bubble', tending to floating rice paddies in the sky, wearing a traditional straw hat with advanced sensors, surrounded by flying fish that leave trails of sparkles. [trigger]"
 ]
 
 css = """
@@ -534,6 +412,12 @@ footer {
     color: var(--primary-color) !important;
     margin-bottom: 1rem !important;
 }
+.tab-nav {
+    margin-bottom: 1rem !important;
+    background-color: var(--panel-background) !important;
+    border-radius: var(--border-radius) !important;
+    overflow: hidden !important;
+}
 """
 
 with gr.Blocks(css=css, analytics_enabled=False, theme="soft") as demo:
@@ -545,98 +429,155 @@ with gr.Blocks(css=css, analytics_enabled=False, theme="soft") as demo:
         </div>
         ''')
         
-        with gr.Row():
-            with gr.Column(scale=3):
-                with gr.Group(elem_classes="container"):
-                    prompt = gr.Textbox(
-                        label="Enter your imagination",
-                        placeholder="Describe your Ghibli-style image here...",
-                        lines=3
-                    )
-                    # Text Rendering 입력란
-                    text_rendering = gr.Textbox(
-                        label="Text Rendering (Multilingual: English, Korean...)",
-                        placeholder="Man saying '안녕' in 'speech bubble'",
-                        lines=1
-                    )
-                    
-                    with gr.Row():
-                        run_button = gr.Button("✨ Generate Image", elem_classes="primary")
-                        clear_button = gr.Button("Clear", elem_classes="secondary")
-                    
-                    with gr.Accordion("Advanced Settings", open=False, elem_classes="settings-accordion"):
-                        with gr.Row():
-                            seed = gr.Slider(
-                                label="Seed",
-                                minimum=0,
-                                maximum=MAX_SEED,
-                                step=1,
-                                value=42,
-                            )
-                            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-                        with gr.Row():
-                            width = gr.Slider(
-                                label="Width",
-                                minimum=256,
-                                maximum=MAX_IMAGE_SIZE,
-                                step=32,
-                                value=1024,
-                            )
-                            height = gr.Slider(
-                                label="Height",
-                                minimum=256,
-                                maximum=MAX_IMAGE_SIZE,
-                                step=32,
-                                value=768,
-                            )
-                        with gr.Row():
-                            guidance_scale = gr.Slider(
-                                label="Guidance scale",
-                                minimum=0.0,
-                                maximum=10.0,
-                                step=0.1,
-                                value=3.5,
-                            )
-                        with gr.Row():
-                            num_inference_steps = gr.Slider(
-                                label="Steps",
-                                minimum=1,
-                                maximum=50,
-                                step=1,
-                                value=30,
-                            )
-                            lora_scale = gr.Slider(
-                                label="LoRA scale",
-                                minimum=0.0,
-                                maximum=1.0,
-                                step=0.1,
-                                value=1.0,
+        with gr.Tabs() as mode_tabs:
+            # 텍스트-이미지 생성 탭
+            with gr.TabItem("Text-to-Image") as text_to_image_tab:
+                with gr.Row():
+                    with gr.Column(scale=3):
+                        with gr.Group(elem_classes="container"):
+                            prompt = gr.Textbox(
+                                label="Enter your imagination",
+                                placeholder="Describe your Ghibli-style image here...",
+                                lines=3
                             )
-                
-                with gr.Group(elem_classes="container"):
-                    gr.Markdown("### ✨ Example Prompts")
-                    examples_html = '\n'.join([f'<div class="example-prompt">{ex}</div>' for ex in examples])
-                    example_container = gr.HTML(examples_html)
+                            
+                            with gr.Row():
+                                run_button = gr.Button("✨ Generate Image", elem_classes="primary")
+                                clear_button = gr.Button("Clear", elem_classes="secondary")
+                            
+                            with gr.Accordion("Advanced Settings", open=False, elem_classes="settings-accordion"):
+                                with gr.Row():
+                                    seed = gr.Slider(
+                                        label="Seed",
+                                        minimum=0,
+                                        maximum=MAX_SEED,
+                                        step=1,
+                                        value=42,
+                                    )
+                                    randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+                                with gr.Row():
+                                    width = gr.Slider(
+                                        label="Width",
+                                        minimum=256,
+                                        maximum=MAX_IMAGE_SIZE,
+                                        step=32,
+                                        value=1024,
+                                    )
+                                    height = gr.Slider(
+                                        label="Height",
+                                        minimum=256,
+                                        maximum=MAX_IMAGE_SIZE,
+                                        step=32,
+                                        value=768,
+                                    )
+                                with gr.Row():
+                                    guidance_scale = gr.Slider(
+                                        label="Guidance scale",
+                                        minimum=0.0,
+                                        maximum=10.0,
+                                        step=0.1,
+                                        value=3.5,
+                                    )
+                                with gr.Row():
+                                    num_inference_steps = gr.Slider(
+                                        label="Steps",
+                                        minimum=1,
+                                        maximum=50,
+                                        step=1,
+                                        value=30,
+                                    )
+                                    lora_scale = gr.Slider(
+                                        label="LoRA scale",
+                                        minimum=0.0,
+                                        maximum=1.0,
+                                        step=0.1,
+                                        value=1.0,
+                                    )
+                        
+                        with gr.Group(elem_classes="container"):
+                            gr.Markdown("### ✨ Example Prompts")
+                            examples_html = '\n'.join([f'<div class="example-prompt">{ex}</div>' for ex in examples])
+                            example_container = gr.HTML(examples_html)
+                    
+                    with gr.Column(scale=4):
+                        with gr.Group(elem_classes="container"):
+                            generation_status = gr.HTML('<div class="status-complete">Ready to generate</div>')
+                            result = gr.Image(label="Generated Image", elem_id="result-image")
+                            seed_text = gr.Number(label="Used Seed", value=42)
             
-            with gr.Column(scale=4):
-                with gr.Group(elem_classes="container"):
-                    generation_status = gr.HTML('<div class="status-complete">Ready to generate</div>')
-                    result = gr.Image(label="Generated Image", elem_id="result-image")
-                    seed_text = gr.Number(label="Used Seed", value=42)
-    
-    with gr.Tabs(elem_classes="tabs") as tabs:
-        with gr.TabItem("Gallery"):
-            with gr.Group(elem_classes="container"):
-                gallery_header = gr.Markdown("### 🖼️ Your Generated Masterpieces")
+            # 이미지-이미지 변환 탭
+            with gr.TabItem("Image-to-Image") as image_to_image_tab:
                 with gr.Row():
-                    refresh_btn = gr.Button("🔄 Refresh Gallery", elem_classes="secondary")
-                generated_gallery = gr.Gallery(
-                    label="Generated Images",
-                    columns=3,
-                    value=load_generated_images(),
-                    height="500px",
-                    elem_classes="gallery-item"
-                )
+                    with gr.Column(scale=3):
+                        with gr.Group(elem_classes="container"):
+                            upload_image = gr.Image(
+                                label="Upload your image",
+                                type="pil",
+                                elem_id="upload-image"
+                            )
+                            img2img_prompt = gr.Textbox(
+                                label="Optional: Describe additional details",
+                                placeholder="Add details or leave empty for default Ghibli style...",
+                                lines=2
+                            )
+                            
+                            with gr.Row():
+                                process_button = gr.Button("🎨 Process Image", elem_classes="primary")
+                                clear_img_button = gr.Button("Clear", elem_classes="secondary")
+                            
+                            with gr.Accordion("Advanced Settings", open=False, elem_classes="settings-accordion"):
+                                with gr.Row():
+                                    img2img_seed = gr.Slider(
+                                        label="Seed",
+                                        minimum=0,
+                                        maximum=MAX_SEED,
+                                        step=1,
+                                        value=42,
+                                    )
+                                    img2img_random_seed = gr.Checkbox(label="Randomize seed", value=True)
+                                with gr.Row():
+                                    img2img_guidance_scale = gr.Slider(
+                                        label="Guidance scale",
+                                        minimum=0.0,
+                                        maximum=10.0,
+                                        step=0.1,
+                                        value=3.5,
+                                    )
+                                with gr.Row():
+                                    img2img_steps = gr.Slider(
+                                        label="Steps",
+                                        minimum=1,
+                                        maximum=50,
+                                        step=1,
+                                        value=30,
+                                    )
+                                    img2img_lora_scale = gr.Slider(
+                                        label="LoRA scale",
+                                        minimum=0.0,
+                                        maximum=1.0,
+                                        step=0.1,
+                                        value=1.0,
+                                    )
+                    
+                    with gr.Column(scale=4):
+                        with gr.Group(elem_classes="container"):
+                            img2img_status = gr.HTML('<div class="status-complete">Ready to process</div>')
+                            img2img_result = gr.Image(label="Processed Image", elem_id="img2img-result")
+                            img2img_seed_text = gr.Number(label="Used Seed", value=42)
+        
+        with gr.Group(elem_classes="container"):
+            with gr.Tabs(elem_classes="tabs") as gallery_tabs:
+                with gr.TabItem("Gallery"):
+                    gallery_header = gr.Markdown("### 🖼️ Your Generated Masterpieces")
+                    with gr.Row():
+                        refresh_btn = gr.Button("🔄 Refresh Gallery", elem_classes="secondary")
+                    generated_gallery = gr.Gallery(
+                        label="Generated Images",
+                        columns=3,
+                        value=load_generated_images(),
+                        height="500px",
+                        elem_classes="gallery-item"
+                    )
 
     ###########################################################################
     # Gradio Helper Functions
@@ -645,13 +586,22 @@ with gr.Blocks(css=css, analytics_enabled=False, theme="soft") as demo:
         return load_generated_images()
 
     def clear_output():
-        return "", gr.update(value=None), seed, '<div class="status-complete">Ready to generate</div>', ""
+        return "", gr.update(value=None), seed, '<div class="status-complete">Ready to generate</div>'
+    
+    def clear_img2img_output():
+        return None, "", img2img_seed, '<div class="status-complete">Ready to process</div>', None
     
     def before_generate():
         return '<div class="status-generating">Generating image...</div>'
     
+    def before_process_image():
+        return '<div class="status-generating">Processing image...</div>'
+    
     def after_generate(image, seed_num, gallery):
         return image, seed_num, gallery, '<div class="status-complete">Generation complete!</div>'
+    
+    def after_process_image(image, seed_num, gallery):
+        return image, seed_num, gallery, '<div class="status-complete">Processing complete!</div>'
 
     ###########################################################################
     # Gradio Event Wiring
@@ -662,25 +612,17 @@ with gr.Blocks(css=css, analytics_enabled=False, theme="soft") as demo:
         outputs=generated_gallery,
     )
     
+    # 텍스트-이미지 생성 이벤트
     clear_button.click(
         fn=clear_output,
         inputs=None,
-        outputs=[prompt, result, seed_text, generation_status, text_rendering]
+        outputs=[prompt, result, seed_text, generation_status]
     )
     
-    # 1) 상태 표시
-    # 2) 텍스트 추출
-    # 3) 이미지 생성
-    # 4) 상태 업데이트
-    # 5) 텍스트 렌더링(있다면)
     run_button.click(
         fn=before_generate,
         inputs=None,
         outputs=generation_status,
-    ).then(
-        fn=extract_text_for_rendering,  # 추가: 텍스트 추출 함수
-        inputs=prompt,
-        outputs=text_rendering,  # 추출된 텍스트로 텍스트 렌더링 필드 업데이트
     ).then(
         fn=inference,
         inputs=[
@@ -698,21 +640,12 @@ with gr.Blocks(css=css, analytics_enabled=False, theme="soft") as demo:
         fn=after_generate,
         inputs=[result, seed_text, generated_gallery],
         outputs=[result, seed_text, generated_gallery, generation_status],
-    ).then(
-        fn=apply_text_rendering,
-        inputs=[result, text_rendering],
-        outputs=result
     )
     
-    # prompt submit 시에도 동일한 체인 실행
     prompt.submit(
         fn=before_generate,
         inputs=None,
         outputs=generation_status,
-    ).then(
-        fn=extract_text_for_rendering,  # 추가: 텍스트 추출 함수
-        inputs=prompt,
-        outputs=text_rendering,  # 추출된 텍스트로 텍스트 렌더링 필드 업데이트
     ).then(
         fn=inference,
         inputs=[
@@ -730,10 +663,35 @@ with gr.Blocks(css=css, analytics_enabled=False, theme="soft") as demo:
         fn=after_generate,
         inputs=[result, seed_text, generated_gallery],
         outputs=[result, seed_text, generated_gallery, generation_status],
+    )
+    
+    # 이미지-이미지 변환 이벤트
+    clear_img_button.click(
+        fn=clear_img2img_output,
+        inputs=None,
+        outputs=[upload_image, img2img_prompt, img2img_seed_text, img2img_status, img2img_result]
+    )
+    
+    process_button.click(
+        fn=before_process_image,
+        inputs=None,
+        outputs=img2img_status,
+    ).then(
+        fn=process_uploaded_image,
+        inputs=[
+            upload_image,
+            img2img_prompt,
+            img2img_seed,
+            img2img_random_seed,
+            img2img_guidance_scale,
+            img2img_steps,
+            img2img_lora_scale,
+        ],
+        outputs=[img2img_result, img2img_seed_text, generated_gallery],
     ).then(
-        fn=apply_text_rendering,
-        inputs=[result, text_rendering],
-        outputs=result
+        fn=after_process_image,
+        inputs=[img2img_result, img2img_seed_text, generated_gallery],
+        outputs=[img2img_result, img2img_seed_text, generated_gallery, img2img_status],
     )
     
     # JS로 예시 prompt 클릭 시 자동 채우기
@@ -756,7 +714,7 @@ with gr.Blocks(css=css, analytics_enabled=False, theme="soft") as demo:
     """)
 
 ###############################################################################
-# 7. 실행
+# 4. 실행
 ###############################################################################
 try:
     demo.queue(concurrency_count=1, max_size=20)